CytoAtlas

Pan-Disease Single-Cell Cytokine Activity Atlas
Date: February 14, 2026

Executive Summary

CytoAtlas is a comprehensive computational resource that maps cytokine and secreted protein signaling activity across ~29 million human cells and ~31,000 bulk RNA-seq samples from six independent datasets: two bulk RNA-seq resources (GTEx, TCGA) and four single-cell compendia (CIMA, Inflammation Atlas, scAtlas, parse_10M) spanning healthy donors, inflammatory diseases, cancers, and cytokine perturbations. The system uses linear ridge regression against experimentally derived signature matrices to infer activity — producing fully interpretable, conditional z-scores rather than black-box predictions.

29MTotal Cells
31KBulk Samples
6Datasets
1,213Signatures
262API Endpoints
12Web Pages

Key results:

  • 1,213 signatures (43 CytoSig + 1,170 SecAct), plus 178 cell-type-specific LinCytoSig variants, validated across 6 independent datasets
  • Spearman correlations reach ρ=0.6–0.9 for well-characterized cytokines (IL1B, TNFA, VEGFA, TGFB family)
  • Cross-dataset consistency demonstrates signatures generalize across CIMA, Inflammation Atlas Main, scAtlas, GTEx, and TCGA
  • SecAct achieves the highest median correlations in 5 of 6 datasets (independence-corrected median ρ=0.31–0.46)

1. System Architecture and Design Rationale

System Architecture
System Architecture. CytoAtlas platform: users interact via a React SPA through Nginx and FastAPI (17 routers, JWT auth). The API serves two primary backends — a Data Query Service (DuckDB, 3 databases, 80+ tables) and an AI Chat Service with dual LLM (Mistral-Small-24B via vLLM + Claude fallback), RAG (LanceDB + MiniLM), and 22 data tools. An offline GPU pipeline (SLURM/A100) performs batch activity inference.

1.1 Architecture and Processing

Linear interpretability over complex models. Ridge regression (L2-regularized linear regression) was chosen deliberately over methods like autoencoders, graph neural networks, or foundation models. The resulting activity z-scores are conditional on the specific genes in the signature matrix, meaning every prediction can be traced to a weighted combination of known gene responses. This is critical for biological interpretation — a scientist can ask “which genes drive the IFNG activity score in this sample?” and get a direct answer.

Reproducibility through separation of concerns. The system is divided into independent components, each chosen for the constraints of HPC/SLURM infrastructure:

ComponentTechnologyPurposeRationale
PipelinePython + CuPy (GPU)Activity inference10–34x speedup over NumPy; batch-streams H5AD files (500K–1M cells/batch) with projection matrix held on GPU; automatic CPU fallback when GPU unavailable
StorageDuckDB (3 databases, 68 tables)Columnar analyticsSingle-file databases require no server — essential on HPC where database servers are unavailable; each database regenerates independently without affecting others
APIFastAPI (262 endpoints)RESTful data accessAsync I/O for concurrent DuckDB queries; automatic OpenAPI documentation; Pydantic request validation; lifespan management for resource initialization
FrontendReact 19 + TypeScriptInteractive exploration (12 pages)Migrated from 25K-line vanilla JS SPA to 11.4K lines (54% reduction) with type safety, component reuse, and lazy-loaded routing

Processing scale. Ridge regression (λ=5×105) is applied using secactpy.ridge() against each signature matrix. For single-cell data, expression is first aggregated to pseudobulk (donor or donor×celltype level), then genes are intersected with the signature matrix (CytoSig: ~4,860 genes; SecAct: ~7,450 genes). The resulting z-scored activity coefficients are compared to target gene expression via Spearman correlation across donors.

DatasetCells/SamplesProcessing TimeHardware
GTEx19,788 bulk samples~10minA100 80GB
TCGA11,069 bulk samples~10minA100 80GB
CIMA6.5M cells~2hA100 80GB
Inflammation Atlas (main/val/ext)6.3M cells~2hA100 80GB
scAtlas Normal2.3M cells~1hA100 80GB
scAtlas Cancer4.1M cells~1hA100 80GB
parse_10M9.7M cells~3hA100 80GB

Total: ~29M single cells + ~31K bulk RNA-seq samples, processed through ridge regression against 3 signature matrices (CytoSig: 43 cytokines, LinCytoSig: 178 cell-type-specific, SecAct: 1,170 secreted proteins). Processing Time = wall-clock time for full activity inference on a single NVIDIA A100 GPU (80 GB VRAM). For bulk datasets (GTEx/TCGA), ridge regression is applied with within-tissue/within-cancer mean centering to remove tissue-level variation. See Section 2.1 for per-dataset details.

1.2 Validation Strategy

CytoAtlas validates at four aggregation levels, each testing whether predicted activity correlates with target gene expression (Spearman ρ) across independent samples:

LevelDescriptionDatasetsReport Section
Donor pseudobulkOne value per donor, averaging across cell typesCIMA, Inflammation Atlas Main, scAtlas Normal/Cancer§4.1, §4.3
Donor × cell-typeStratified by cell type within each donorCIMA, Inflammation Atlas Main, scAtlas Normal/Cancer§4.7
Per-tissue / per-cancerMedian-of-medians across tissues or cancer typesGTEx (29 tissues), TCGA (33 cancer types)§4.2, §4.3
Cross-platformBulk vs pseudobulk concordance per tissue/cancerGTEx vs scAtlas Normal, TCGA vs scAtlas Cancer§4.4

All statistics use independence-corrected values — preventing inflation from repeated measures across tissues, cancer types, or cell types. CytoSig vs SecAct comparisons use Mann-Whitney U (total) and Wilcoxon signed-rank (32 matched targets) with BH-FDR correction. See Section 3.3 for the validation philosophy and Section 4 for full results.

Why independence correction matters: Pooling across tissues or cancer types inflates correlations through confounding. For example, GTEx pooled CytoSig median ρ (0.211) is 40% higher than the independence-corrected by-tissue value (0.151); SecAct shows +30% inflation (0.394 vs 0.304). All results in this report use the corrected values. For a detailed comparison of pooled vs independent levels, including inflation magnitude and finer cell-type stratification, see the Section 4.1 statistical supplement.

Figure 1: Dataset Overview
Figure 1. CytoAtlas overview. Data sources (4 single-cell compendia, 2 bulk RNA-seq resources) are processed through ridge regression against 3 signature matrices, then validated across 7 complementary analyses (§4.1–§4.7).

2. Dataset Catalog

2.1 Datasets and Scale [detailed analytics]

#DatasetTypeCells/SamplesDonorsCell TypesReference
1GTExBulk RNA-seq19,788 samples946 donorsGTEx Consortium, v11
2TCGABulk RNA-seq11,069 samples10,274 donorsTCGA PanCancer
3CIMAscRNA-seq6,484,974421 donors27 L2 / 100+ L3J. Yin et al., Science, 2026
4Inflammation Atlas MainscRNA-seq4,918,140817 samples*66+Jimenez-Gracia et al., Nature Medicine, 2026
5Inflammation Atlas ValscRNA-seq849,922144 samples*66+Validation cohort
6Inflammation Atlas ExtscRNA-seq572,87286 samples*66+External cohort
7scAtlas NormalscRNA-seq2,293,951317 donors102 subClusterQ. Shi et al., Nature, 2025
8scAtlas CancerscRNA-seq4,146,975717 donors (601 tumor-only)162 cellType1Q. Shi et al., Nature, 2025
9parse_10MscRNA-seq9,697,97412 donors × 90 cytokines (+PBS control)18 PBMC typesOesinghaus et al., bioRxiv, 2026

Grand total: ~29 million single cells + ~31K bulk samples from 6 independent studies (9 datasets), 100+ cell types.

* Inflammation Atlas does not provide donor-level identifiers; the 817/144/86 values are sample counts. The donor–sample relationship is unknown, so correlations use sampleID as the independent unit.

2.2 Disease and Condition Categories

CIMA (421 healthy donors): Healthy population atlas with paired blood biochemistry (19 markers: ALT, AST, glucose, lipid panel, etc.) and plasma metabolomics (1,549 features). Enables age, BMI, sex, and smoking correlations with cytokine activity.

Inflammation Atlas (20 diseases): RA, SLE, Sjogren's, PSA, Crohn's, UC, COVID-19, Sepsis, HIV, HBV, BRCA, CRC, HNSCC, NPC, COPD, Cirrhosis, MS, Asthma, Atopic Dermatitis

scAtlas Normal (317 donors): 35 organs, 12 tissues with ≥20 donors for per-organ stratification (Breast 124, Lung 97, Colon 65, Heart 52, Liver 43, etc.)

scAtlas Cancer (717 donors, 601 tumor-only): 29 cancer types, 11 with ≥20 tumor-only donors for per-cancer stratification (HCC 88, PAAD 58, CRC 51, ESCA 48, HNSC 39, LUAD 36, NPC 36, KIRC 31, BRCA 30, ICC 29, STAD 27)

parse_10M: 90 cytokines × 12 donors — independent in vitro perturbation dataset for comparison. A considerable portion of cytokines (~58%) are produced in E. coli, with the remainder from insect (Sf21, 12%) and mammalian (CHO, NS0, HEK293, ~30%) expression systems. Because exogenous perturbagens may induce effects differing from endogenously produced cytokines, parse_10M serves as an independent comparison rather than strict ground truth. CytoSig/SecAct has a potential advantage in this regard, as it infers relationships directly from physiologically relevant samples.

2.3 Signature Matrices

MatrixTargetsConstructionReference
CytoSig43 cytokinesMedian log2FC across all experimental bulk RNA-seqJiang et al., Nature Methods, 2021
LinCytoSig178 (45 cell types × 1–13 cytokines)Cell-type-stratified median from CytoSig database (methodology)This work
SecAct1,170 secreted proteinsMedian global Moran's I across 1,000 Visium datasetsRu et al., Nature Methods, 2026 (in press)

3. Scientific Value Proposition

3.1 What Makes CytoAtlas Different from Deep Learning Approaches?

Most single-cell analysis tools use complex models (VAEs, GNNs, transformers) that produce aggregated, non-linear representations difficult to interpret biologically. CytoAtlas takes the opposite approach:

PropertyCytoAtlas (Ridge Regression)Typical DL Approach
ModelLinear (z = Xβ + ε)Non-linear (multi-layer NN)
InterpretabilityEvery gene's contribution is a coefficientFeature importance approximated post-hoc
ConditionalityActivity conditional on specific gene setLatent space mixes all features
ConfidencePermutation-based z-scores with CIOften point estimates only
GeneralizationTested across 6 independent cohortsOften held-out splits of same cohort
BiasTransparent — limited by signature matrix genesHidden in architecture and training data

The key insight: CytoAtlas is not trying to replace DL-based tools. It provides an orthogonal, complementary signal that a human scientist can directly inspect. When CytoAtlas says "IFNG activity is elevated in CD8+ T cells from RA patients," you can verify this by checking the IFNG signature genes in those cells.

3.2 What Scientific Questions Does CytoAtlas Answer?

  1. Which cytokines are active in which cell types across diseases? — IL1B/TNFA in monocytes/macrophages, IFNG in CD8+ T and NK cells, IL17A in Th17, VEGFA in endothelial/tumor cells, TGFB family in stromal cells — quantified across 20 diseases, 35 organs, and 15 cancer types.
  2. Are cytokine activities consistent across independent cohorts? — Yes. IL1B, TNFA, VEGFA, and TGFB family show consistent positive correlations across all 6 validation datasets (Figure 8).
  3. Does cell-type-specific biology matter for cytokine inference? — For select immune types, yes: LinCytoSig improves prediction for Basophils (+0.21 Δρ), NK cells (+0.19), and DCs (+0.18), but global CytoSig wins overall (Figures 11–12).
  4. Which secreted proteins beyond cytokines show validated activity? — SecAct (1,170 targets) achieves the highest correlations across all datasets (median ρ=0.33–0.49), with novel validated targets like Activin A (ρ=0.98), CXCL12 (ρ=0.92), and BMP family (Figure 13).
  5. Can we predict treatment response from cytokine activity? — We are incorporating cytokine-blocking therapy outcomes from bulk RNA-seq to test whether predicted cytokine activity associates with therapy response. Additionally, Inflammation Atlas responder/non-responder labels enable treatment response prediction using cytokine activity profiles as features.

3.3 Validation Philosophy

CytoAtlas validates against a simple but powerful principle: if CytoSig predicts high IFNG activity for a sample, that sample should have high IFNG gene expression. This expression-activity correlation is computed via Spearman rank correlation across donors/samples.

This is a conservative validation — it only captures signatures where the target gene itself is expressed. Signatures that act through downstream effectors would not be captured, meaning our validation underestimates true accuracy.


4. Validation Results

4.1 Overall Performance Summary [Full Details]

PRIMARY independent level: The summary table above reports results at each dataset’s PRIMARY independent level — the aggregation level where samples are fully independent (each donor counted once). This ensures correlation statistics are not inflated by donor duplication. See the “Primary Level” column for each dataset’s level.

How “N Targets” is determined: A target is included in the validation for a given atlas only if (1) the target’s signature genes overlap sufficiently with the atlas gene expression matrix, and (2) the target gene itself is expressed in enough samples to compute a meaningful Spearman correlation. Targets whose gene is absent or not detected in a dataset are excluded. CytoSig defines 43 cytokines and SecAct defines 1,170 secreted proteins. Inflammation Atlas Main retains only 33 of 43 CytoSig targets and 805 of 1,170 SecAct targets because 10 cytokine genes (BDNF, BMP4, CXCL12, GCSF, IFN1, IL13, IL17A, IL36, IL4, WNT3A) are not sufficiently expressed in these blood/PBMC samples.

Stratified levels (GTEx by_tissue, TCGA primary_by_cancer): Correlations are computed within each tissue/cancer type (ensuring independence), then summarized across groups. N Targets counts unique targets at the “all” aggregate level. Finer per-tissue or per-cancer breakdowns are available in Section 4.3 below.

4.2 Cross-Dataset Comparison: CytoSig vs SecAct [Statistical Methods]

Figure 2. Spearman ρ distributions across datasets for CytoSig (43 targets) vs SecAct (1,170 targets). Independence-corrected: GTEx/TCGA use median-of-medians. Mann-Whitney U test p-values shown above each dataset.

Why does SecAct appear to underperform CytoSig in Inflammation Atlas Main?

This is a composition effect, not a genuine performance gap, confirmed by two complementary statistical tests:

Total comparison (Mann–Whitney U test): Compares the full ρ distributions of CytoSig (43 cytokine signatures) vs SecAct (~1,170 secreted protein signatures) using independence-corrected values. For GTEx/TCGA, each target’s representative ρ is the median across per-tissue/cancer values (median-of-medians); for other datasets, donor_only/tumor_only ρ is used directly. SecAct achieves a significantly higher median ρ in 5 of 6 datasets (GTEx: p = 4.76 × 10−4; TCGA: p = 2.85 × 10−3; CIMA: p = 3.18 × 10−2; scAtlas Normal: p = 1.04 × 10−4; scAtlas Cancer: p = 1.06 × 10−5). Inflammation Atlas Main is the sole exception (U = 14,101, p = 0.548, not significant) and the only dataset where CytoSig’s median ρ (0.323) exceeds SecAct’s (0.173).

Matched comparison (Wilcoxon signed-rank test): Restricts to the 32 targets shared between both methods (22 direct + 10 alias-resolved), each target serving as its own control. SecAct’s median ρ is consistently higher across all 6 datasets, reaching significance in 5 (GTEx: p = 3.54 × 10−5; TCGA: p = 3.24 × 10−6; CIMA: p = 2.28 × 10−2; scAtlas Normal: p = 3.54 × 10−5; scAtlas Cancer: p = 3.54 × 10−5). Inflammation Atlas Main is not significant (p = 0.141).

Inflammation Atlas Main is largely blood-derived, so many SecAct targets that perform well in multi-organ contexts contribute near-zero or negative correlations here. In fact, 99 SecAct targets are negative only in Inflammation Atlas Main but positive in all other datasets, reflecting tissue-specific expression limitations rather than inference failure. The “Matched” tab above demonstrates the fair comparison on equal footing.

4.3 Per-Tissue and Per-Cancer Stratified Validation [Statistical Methods]

Figure 3. Per-tissue/cancer CytoSig vs SecAct median Spearman ρ comparison. BH-FDR corrected significance: *** q<0.001, ** q<0.01, * q<0.05.

Stratified validation: Instead of aggregating tissues/cancers into a single median-of-medians, this view shows the CytoSig vs SecAct comparison within each individual tissue (GTEx) or cancer type (TCGA). Mann-Whitney U test (Total tab: all targets) and Wilcoxon signed-rank test (Matched tab: 32 shared targets) with BH-FDR correction across all strata within each dataset.

Key insight: On the 32 matched targets, SecAct wins direction in every stratum — 29/29 GTEx tissues and 33/33 TCGA cancer types — with 25/29 and 31/33 reaching significance (q<0.05). This unanimous result across 62 independent strata rules out Simpson’s paradox. On total targets, SecAct wins in 28/29 GTEx tissues (21 significant) and 30/33 TCGA cancers (15 significant); the few CytoSig-favored strata (Brain in GTEx; Kidney Chromophobe, Ovarian, Uveal Melanoma in TCGA) are all non-significant. Since SecAct outperforms CytoSig on the same 32 cytokines, the advantage is not about target breadth but about signature quality. SecAct’s spatial-transcriptomics-derived signatures (Visium) capture tissue-context-dependent cytokine regulation that CytoSig’s case-control cytokine treatment experiments might not capture. The advantage is largest in tissues with complex cellular microenvironments (GTEx: Small Intestine Δ=+0.47, Esophagus +0.41; TCGA: Testicular +0.33, Cervical +0.32) and smallest in homogeneous contexts (GTEx: Breast +0.001, Pituitary +0.06; TCGA: Brain Glioma +0.06, Kidney Chromophobe +0.09).

4.4 Cross-Platform Comparison: Bulk vs Pseudobulk [Statistical Methods]

Figure 4. Cross-platform concordance: per-target Spearman ρ distributions from bulk RNA-seq (GTEx/TCGA) vs single-cell pseudobulk (scAtlas) for matching tissues/cancer types. Side-by-side boxplots show the correlation distribution for each tissue/cancer.

Cross-platform concordance: This section tests whether expression–activity relationships replicate across measurement technologies. For each tissue (GTEx) or cancer type (TCGA), we compute per-target Spearman ρ from bulk RNA-seq data and compare it to the same target’s ρ from single-cell pseudobulk data (scAtlas). Wilcoxon signed-rank tests (paired by target) with BH-FDR correction assess whether ρ values differ between platforms.

Key finding: Using all targets, SecAct shows significant bulk–pseudobulk differences in most strata (11/13 GTEx tissues, 5/11 TCGA cancers), while CytoSig shows almost none (1/13, 0/11). However, the Matched tabs reveal this is a statistical power effect, not a signal quality difference: when restricted to the same 32 shared targets, both CytoSig and SecAct show no significant platform differences (0/13 and 0/13 for GTEx; 0/11 and 1/11 for TCGA). The apparent platform sensitivity in SecAct’s full panel is a statistical power effect, not a signal quality difference: matched and unmatched SecAct targets show the same per-target platform shift (mean |Δ| = 0.298 vs 0.302, Mann–Whitney p = 0.82), but SecAct’s ~1,000 paired targets per tissue provide 25× more observations than CytoSig’s ~40, easily detecting the same tiny systematic shift (Δ ≈ 0.03) that CytoSig lacks power to detect. Core cytokine targets are platform-robust.

4.5 Best and Worst Correlated Targets

Figure 5. Top 15 (best) and bottom 15 (worst) correlated targets. Select signature type and dataset from dropdowns.

Consistently well-correlated targets (ρ > 0.3 across multiple datasets):

Consistently poorly correlated targets (ρ < 0 in multiple datasets):

Gene mapping verified: All four targets are correctly mapped (CD40L→CD40LG, TRAIL→TNFSF10, LTA→LTA, HGF→HGF). No gene ID confusion exists. The poor correlations reflect specific molecular mechanisms:

TargetGeneDominant MechanismContributing Factors
CD40LCD40LG Platelet-derived sCD40L invisible to scRNA-seq (~95% of circulating CD40L); ADAM10-mediated membrane shedding Unstable mRNA (3′-UTR destabilizing element); transient expression kinetics (peak 6–8h post-activation); paracrine disconnect (T cell → B cell/DC)
TRAILTNFSF10 Three decoy receptors (DcR1/TNFRSF10C, DcR2/TNFRSF10D, OPG/TNFRSF11B) competitively sequester ligand without signaling Non-functional splice variants (TRAIL-beta, TRAIL-gamma lack exon 3) inflate mRNA counts; cathepsin E-mediated shedding; apoptosis-induced survival bias in scRNA-seq data
LTALTA Obligate heteromeric complex with LTB: the dominant form (LTα1β2) requires LTB co-expression and signals through LTBR, not TNFR1/2 Mathematical collinearity with TNFA in ridge regression (LTA3 homotrimer binds the same TNFR1/2 receptors as TNF-α); 7 known splice variants; low/transient expression
HGFHGF Obligate mesenchymal-to-epithelial paracrine topology: HGF produced by fibroblasts/stellate cells, MET receptor on epithelial cells Secreted as inactive pro-HGF requiring proteolytic cleavage by HGFAC/uPA (post-translational activation is rate-limiting); ECM/heparin sequestration creates stored protein pool invisible to transcriptomics

Key insight: None of these targets have isoforms or subunits mapping to different gene IDs that would cause gene ID confusion. The poor correlations are supposedly driven by post-translational regulation (membrane shedding, proteolytic activation, decoy receptor sequestration), paracrine signaling topology (producer and responder cells are different cell types), and heteromeric complex dependence (LTA requires LTB). These represent fundamental limitations of correlating ligand mRNA abundance and predicted activity as validation strategy of cytokine activity prediction model.

However, SecAct rescues all four targets. The poor correlations above are CytoSig-specific, not universal. SecAct achieves strong positive correlations for every one of these targets (mean ρ across datasets):

TargetCytoSig GeneCytoSig Mean ρSecAct GeneSecAct Mean ρ
CD40LCD40LG−0.006CD40LG+0.420
TRAILTNFSF10−0.016TNFSF10+0.418
LTALTA−0.019LTA+0.474
HGFHGF+0.034HGF+0.540

The key difference is how the signature matrices are constructed. CytoSig derives signatures from log2 fold-change in cytokine stimulation experiments (in vitro), which fails when the relationship between ligand mRNA and downstream activity is confounded by post-translational regulation, decoy receptors, or paracrine topology. SecAct derives signatures from spatial co-expression correlations (Moran’s I across 1,000+ Visium spatial transcriptomics datasets), which captures the actual tissue-level gene–protein relationships regardless of whether the signaling mechanism involves membrane shedding, proteolytic activation, or cross-cell-type paracrine signaling. Select “SecAct” in the dropdown above to verify these correlations interactively.

4.6 Cross-Atlas Consistency

Figure 6. Key cytokine target correlations tracked across 6 independent datasets (donor-level). Solid lines = CytoSig; dashed lines = SecAct. Lines colored by cytokine family. Click legend entries to show/hide targets.

4.7 Effect of Aggregation Level [Statistical Methods]

Figure 7. Effect of cell-type annotation granularity on validation correlations. Total: CytoSig (43 targets) vs SecAct (1,170 targets). Matched: 32 shared targets only. Select atlas from dropdown.

Aggregation levels explained: Pseudobulk profiles are aggregated at increasingly fine cell-type resolution. At coarser levels, each pseudobulk profile averages more cells, yielding smoother expression estimates but masking cell-type-specific signals. At finer levels, each profile is more cell-type-specific but based on fewer cells.

AtlasLevelDescriptionN Cell Types
CIMA Donor OnlyWhole-sample pseudobulk per donor1 (all)
Donor × L1Broad lineages (B, CD4_T, CD8_T, Myeloid, NK, etc.)7
Donor × L2Intermediate (CD4_memory, CD8_naive, DC, Mono, etc.)28
Donor × L3Fine-grained (CD4_Tcm, cMono, Switched_Bm, etc.)39
Donor × L4Finest marker-annotated (CD4_Th17-like_RORC, cMono_IL1B, etc.)73
Inflammation Atlas Main Donor OnlyWhole-sample pseudobulk per donor1 (all)
Donor × L1Broad categories (B, DC, Mono, T_CD4/CD8 subsets, etc.)18
Donor × L2Fine-grained (Th1, Th2, Tregs, NK_adaptive, etc.)65
scAtlas Normal Donor × OrganPer-organ pseudobulk (Bladder, Blood, Breast, Lung, etc.)25 organs
Donor × Organ × CT1Broad cell types within each organ191
Donor × Organ × CT2Fine cell types within each organ356
scAtlas Cancer Tumor OnlyWhole-sample pseudobulk per tumor donor1 (all)
Tumor × CancerPer-cancer type pseudobulk (HCC, PAAD, CRC, etc.)29 types
Tumor × Cancer × CT1Broad cell types within each cancer type~120

4.8 Representative Scatter Plots

Figure 8. Donor-level expression vs predicted activity. Select target, atlas, and signature method from dropdowns.

4.9 Biologically Important Targets Heatmap

Figure 9. Spearman ρ heatmap for biologically important targets across all datasets. Switch between signature types. Hover over cells for details.

How each correlation value is computed: For each (target, atlas) cell, we compute Spearman rank correlation between predicted cytokine activity (ridge regression z-score) and target gene expression across all donor-level pseudobulk samples. Specifically:

  1. Pseudobulk aggregation: For each atlas, gene expression is aggregated to the donor level (one profile per donor or donor × cell type).
  2. Activity inference: Ridge regression (secactpy.ridge, λ=5×105) is applied using the signature matrix (CytoSig: 4,881 genes × 43 cytokines; SecAct: 7,919 genes × 1,170 targets) to predict activity z-scores for each pseudobulk sample.
  3. Correlation: Spearman ρ is computed between the predicted activity z-score and the original expression of the target gene across all donor-level samples within that atlas. A positive ρ means higher predicted activity tracks with higher target gene expression.

GTEx uses per-tissue pseudobulk (median-of-medians across 29 tissues); TCGA uses per-cancer type (median-of-medians across 33 cancers); CIMA/Inflammation Atlas Main use donor-only; scAtlas Normal uses donor-only; scAtlas Cancer uses tumor-only.

4.10 Per-Target Correlation Rankings

Figure 10. Validation: targets ranked by Spearman ρ across all datasets and signature types. Select dataset and signature from dropdowns.

5. CytoSig vs LinCytoSig vs SecAct Comparison

5.1 Method Overview

MethodTargetsGenesSpecificitySelection
CytoSig 43 cytokines4,881 curatedGlobal (all cell types)
LinCytoSig (orig) 178 (45 CT × cytokines)All ~20KCell-type specificMatched cell type
LinCytoSig (gene-filtered) 1784,881 (CytoSig overlap)Cell-type specificMatched cell type
LinCytoSig Best (combined) 43 (1 per cytokine)All ~20KBest CT per cytokineMax combined GTEx+TCGA ρ
LinCytoSig Best (comb+filt) 43 (1 per cytokine)4,881 (CytoSig overlap)Best CT per cytokineMax combined ρ (filtered)
LinCytoSig Best (GTEx) 43 (1 per cytokine)All ~20KBest CT per cytokineMax GTEx ρ
LinCytoSig Best (TCGA) 43 (1 per cytokine)All ~20KBest CT per cytokineMax TCGA ρ
LinCytoSig Best (GTEx+filt) 43 (1 per cytokine)4,881 (CytoSig overlap)Best CT per cytokineMax GTEx ρ (filtered)
LinCytoSig Best (TCGA+filt) 43 (1 per cytokine)4,881 (CytoSig overlap)Best CT per cytokineMax TCGA ρ (filtered)
SecAct 1,170 secreted proteinsSpatial Moran’s IGlobal (all cell types)

Gene filter: LinCytoSig signatures restricted from ~20K to CytoSig’s 4,881 curated genes. Best selection: For each cytokine, test all cell-type-specific LinCytoSig signatures and select the one with the highest bulk RNA-seq correlation. “Combined” uses pooled GTEx+TCGA; “GTEx” and “TCGA” select independently per bulk dataset. “+filt” variants apply the same cell-type selection but restrict to CytoSig gene space. See LinCytoSig Methodology for details.

Figure 11. Ten-way signature method comparison at matched (cell type, cytokine) pair level across 4 combined datasets. All 10 methods are evaluated on the same set of matched pairs per dataset (identical n). Use dropdown to view individual dataset boxplots. For LinCytoSig construction, see LinCytoSig Methodology.

Ten methods compared on identical matched pairs across 4 combined datasets:

  1. CytoSig — 43 cytokines, 4,881 curated genes, global (all cell types)
  2. LinCytoSig (orig) — cell-type-matched signatures, all ~20K genes
  3. LinCytoSig (gene-filtered) — cell-type-matched signatures, restricted to CytoSig’s 4,881 genes
  4. LinCytoSig Best (combined) — best cell-type signature per cytokine (selected by combined GTEx+TCGA bulk ρ), all ~20K genes
  5. LinCytoSig Best (comb+filt) — best combined bulk signature, restricted to 4,881 genes
  6. LinCytoSig Best (GTEx) — best per cytokine selected by GTEx-only bulk ρ, all ~20K genes
  7. LinCytoSig Best (TCGA) — best per cytokine selected by TCGA-only bulk ρ, all ~20K genes
  8. LinCytoSig Best (GTEx+filt) — GTEx-selected best, restricted to 4,881 genes
  9. LinCytoSig Best (TCGA+filt) — TCGA-selected best, restricted to 4,881 genes
  10. SecAct — 1,170 secreted proteins (Moran’s I), subset matching CytoSig targets

Key findings:

  • SecAct achieves the highest median ρ across all 4 combined datasets, benefiting from spatial-transcriptomics-derived signatures.
  • CytoSig outperforms most LinCytoSig variants at donor level, with one notable exception: scAtlas Normal Best-orig (0.298) exceeds CytoSig (0.216).
  • Gene filtering improves LinCytoSig in most datasets (CIMA +102%, Inflammation Atlas Main), confirming noise reduction from restricting the gene space.
  • GTEx-selected best performs comparably to combined-selected in most datasets but slightly better in scAtlas Cancer (0.300 vs 0.275). TCGA-selected best generally underperforms other selection strategies, suggesting GTEx’s broader tissue coverage provides more generalizable selections.
  • Gene filtering of GTEx/TCGA-selected: GTEx+filt and TCGA+filt show mixed results — filtering sometimes improves (e.g., TCGA+filt in Inflammation Atlas Main: 0.260 vs TCGA-orig 0.168) but can also reduce performance, indicating the optimal gene space depends on both the selection dataset and target dataset context.
  • General ranking: SecAct > CytoSig > LinCytoSig Best variants > LinCytoSig (filt) > LinCytoSig (orig), though dataset-specific exceptions exist.

5.2 Effect of Aggregation Level

Methodology: At each cell-type aggregation level (CIMA: L1–L4 = 7–73 cell types; Inflammation: L1–L2; scAtlas: CT1–CT2 = coarse/fine), we match CytoSig, LinCytoSig, and SecAct on identical (cytokine, cell type) pairs — using the exact same pseudobulk samples and identical n for all three methods. For each pair, Spearman ρ measures agreement between predicted activity and target gene expression. If lineage-specific aggregation helps, LinCytoSig should increasingly outperform CytoSig as cell-type resolution increases (L1 → L4).

5.2.1 Distribution at Each Level

Figure 12. Distribution of Spearman ρ at each cell-type aggregation level. All three methods evaluated on identical matched pairs per level. Finer levels (more cell types) should theoretically favor lineage-specific methods.

5.2.2 Summary

n = number of three-way matched pairs. Δρ = LinCytoSig − competitor (negative = LinCytoSig underperforms).

5.2.3 Which Cell Types Benefit?

Aggregated across all datasets at finest celltype level. Green = LinCytoSig wins more; red = LinCytoSig loses more.

5.2.4 Which Cytokines Benefit?

Sorted by mean Δρ vs CytoSig (best to worst).

Key finding: Lineage-specific aggregation provides no systematic advantage at any level.

  • At every level, LinCytoSig underperforms CytoSig (mean Δρ ranges from −0.08 at coarse L1 to −0.02 at fine L4 in CIMA). Finer cell types reduce the gap slightly but never close it.
  • SecAct wins at every level in CIMA and scAtlas. In Inflammation Atlas Main L2, LinCytoSig is nearly tied with SecAct (Δρ = +0.01) but still loses to CytoSig.
  • Per cell type: Only 5 of 43 cell types show consistent LinCytoSig advantage vs CytoSig (NK Cell, Basophil, DC, Trophoblast, Arterial Endothelial). No cell type beats SecAct.
  • Interpretation: CytoSig’s global signature, derived from median log2FC across all cell types, already captures the dominant transcriptional response. Restricting to a single cell type’s response introduces noise from small sample sizes without gaining meaningful lineage specificity. The hypothesis that finer resolution should favor LinCytoSig is not supported by the data.

5.3 SecAct: Breadth Over Depth

Figure 13. Per-celltype mean Δρ (LinCytoSig − CytoSig) aggregated across 4 datasets at donor × celltype level. Orange = LinCytoSig advantage; blue = CytoSig advantage. Error bars show SEM.

6. Key Takeaways for Scientific Discovery

6.1 What CytoAtlas Enables

  1. Quantitative cytokine activity per cell type per disease — 43 CytoSig cytokines + 1,170 SecAct secreted proteins across 29M cells
  2. Cross-disease comparison — same signatures validated across 20 diseases, 35 organs, 15 cancer types
  3. Independent perturbation comparison — parse_10M provides 90 cytokine perturbations × 12 donors × 18 cell types for independent comparison with CytoSig predictions
  4. Multi-level validation — donor, donor × celltype, bulk RNA-seq (GTEx/TCGA), and resampled bootstrap validation across 6 datasets

6.2 Limitations

  1. Linear model: Cannot capture non-linear cytokine interactions
  2. Transcriptomics-only: Post-translational regulation invisible
  3. Signature matrix bias: Underrepresented cell types have weaker signatures
  4. Validation metric: Expression-activity correlation underestimates true accuracy (signatures acting through downstream effectors are not captured)

6.3 Future Directions

  1. scGPT cohort integration (~35M cells)
  2. cellxgene Census integration
  3. Classification of cytokine blocking therapy

7. Appendix: Technical Specifications

A. Computational Infrastructure

B. Statistical Methods